How Does Artificial Intelligence Work?

Curated on Posted on by Stefaan Verhulst

BuiltIn: “Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: “Can machines think?” 

Turing’s paper “Computing Machinery and Intelligence” (1950), and its subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.   

At its core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines.

The expansive goal of artificial intelligence has given rise to many questions and debates. So much so, that no singular definition of the field is universally accepted.  

The major limitation in defining AI as simply “building machines that are intelligent” is that it doesn’t actually explain what artificial intelligence is? What makes a machine intelligent?

In their groundbreaking textbook Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the question by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is “the study of agents that receive percepts from the environment and perform actions.” (Russel and Norvig viii)

Norvig and Russell go on to explore four different approaches that have historically defined the field of AI: 

  1. Thinking humanly
  2. Thinking rationally
  3. Acting humanly 
  4. Acting rationally

The first two ideas concern thought processes and reasoning, while the others deal with behavior. Norvig and Russell focus particularly on rational agents that act to achieve the best outcome, noting “all the skills needed for the Turing Test also allow an agent to act rationally.” (Russel and Norvig 4).

Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT, defines AI as  “algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together.”…(More)”.

Citizens ‘on mute’ in digital public service delivery

Curated on Posted on by Stefaan Verhulst

Blog by Sarah Giest at Data and Policy: “Various countries are digitalizing their welfare system in the larger context of austerity considerations and fraud detection goals, but these changes are increasingly under scrutiny. In short, digitalization of the welfare system means that with the help of mathematical models, data and/or the combination of different administrative datasets, algorithms issue a decision on, for example, an application for social benefits (Dencik and Kaun 2020).

Several examples exist where such systems have led to unfair treatment of welfare recipients. In Europe, the Dutch SyRI system has been banned by court, due to human rights violations in the profiling of welfare recipients, and the UK has found errors in the automated processes leading to financial hardship among citizens. In the United States and Canada, automated systems led to false underpayment or denial of benefits. A recent UN report (2019) even warns that countries are ‘stumbling zombie-like into a digital welfare dystopia’. Further, studies raise alarm that this process of digitalization is done in a way that it not only creates excessive information asymmetry among government and citizens, but also disadvantages certain groups more than others.

A closer look at the Dutch Childcare Allowance case highlights this. In this example, low-income parents were regarded as fraudsters by the Tax Authorities if they had incorrectly filled out any documents. An automated and algorithm-based procedure then also singled out dual-nationality families. The victims lost their allowance without having been given any reasons. Even worse, benefits already received were reclaimed. This led to individual hardship, where financial troubles and the categorization as a fraudster by government led for citizens to a chain of events from unpaid healthcare insurance and the inability to visit a doctor to job loss, potential home loss and mental health concerns (Volkskrant 2020)….(More)”.

Citizen science allows people to ‘really know’ their communities

Curated on Posted on by Stefaan Verhulst

UGAResearch: “Local populations understand their communities best. They’re familiar both with points of pride and with areas that could be improved. But determining the nature of those improvements from best practices, as well as achieving community consensus on implementation, can present a different set of challenges.

Jerry Shannon, associate professor of geography in the Franklin College of Arts & Sciences, worked with a team of researchers to introduce a citizen science approach in 11 communities across Georgia, from Rockmart to Monroe to Millen. This work combines local knowledge with emerging digital technologies to bolster community-driven efforts in multiple communities in rural Georgia. His research was detailed in a paper, “‘Really Knowing’ the Community: Citizen Science, VGI and Community Housing Assessments” published in December in the Journal of Planning Education and Research.

Shannon worked with the Georgia Initiative for Community Housing, managed out of the College of Family and Consumer Sciences (FACS), to create tools for communities to evaluate and launch plans to address their housing needs and revitalization. This citizen science effort resulted in a more diverse and inclusive body of data that incorporated local perspectives.

“Through this project, we hope to further support and extend these community-driven efforts to assure affordable, quality housing,” said Shannon. “Rural communities don’t have the resources internally to do this work themselves. We provide training and tools to these communities.”

As part of their participation in the GICH program, each Georgia community assembled a housing team consisting of elected officials, members of community organizations and housing professionals such as real estate agents. The team recruited volunteers from student groups and religious organizations to conduct so-called “windshield surveys,” where participants work from their vehicle or walk the neighborhoods….(More)”

Process Mapping: a Tool with Many Uses

Curated on Posted on by Stefaan Verhulst

Essay by Jessica Brandt: “Traditionally, process maps are used when one is working on improving a process, but a good process map can serve many purposes. But what is a process map used for and why is this a tool worth learning about? A process map is a tool using a flowchart to illustrate the flow, people, as well as inputs, actions, and outputs of the process in a clear and detailed way. A good process map will reflect the work that is actually done within a given process, not what the intended or imagined workflow might entail. This means in order to build a good process map you should be talking to and learning from the folks that use the process every day, not just the people that oversee the process. Because I see the value behind having a good process map and the many ways you can utilize one to make your work more efficient I want to share with you some of the different ways you can use this versatile tool….(More)”.

Are Repeat Nudges Effective? For Tardy Tax Filers, It Seems So

Curated on Posted on by Stefaan Verhulst

Paper by Nicole Robitaille, Nina Mažar, and Julian House: “While behavioral scientists sometimes aim to nudge one-time actions, such as registering as an organ donor or signing up for a 401K, there are many other behaviors—making healthy food choices, paying bills, filing taxes, getting a flu shot—that are repeated on a daily, monthly, or annual basis. If you want to target these recurrent behaviors, can introducing a nudge once lead to consistent changes in behavior? What if you presented the same nudge several times—would seeing it over and over make its effects stronger, or just the opposite?

Decades of research from behavioral science has taught us a lot about nudges, but the field as a whole still doesn’t have a great understanding of the temporal dimensions of most interventions, including how long nudge effects last and whether or not they remain effective when repeated.

If you want an intervention to lead to lasting behavior change, prior research argues that it should target people’s beliefs, habits or the future costs of engaging in the behavior. Many nudges, however, focus instead on manipulating relatively small factors in the immediate choice environment to influence behavior, such as changing the order in which options are presented. In addition, relatively few field experiments have been able to administer and measure an intervention’s effects more than once, making it hard to know how long the effects of nudges are likely to persist.

While there is some research on what to expect when repeating nudges, the results are mixed. On the one hand, there is an extensive body of research in psychology on habituation, finding that, over time, people show decreased responses to the same stimuli. It wouldn’t be a giant leap to presume that seeing the same nudge again might decrease how much attention we pay to it, and thus hinder its ability to change our behavior. On the other hand, being exposed to the same nudge multiple times might help strengthen desired associations. Research on the mere exposure effect, for example, illustrates how the more times we see something, the more easily it is processed and the more we like it. It is also possible that being nudged multiple times could help foster enduring change, such as through new habit formation. Behavioral nudges aren’t going away, and their use will likely grow among policymakers and practitioners. It is critical to understand the temporal dimensions of these interventions, including how long one-off effects will last and if they will continue to be effective when seen multiple times….(More)”

Data-driven environmental decision-making and action in armed conflict

Curated on Posted on by Stefaan Verhulst

Essay by Wim Zwijnenburg: “Our understanding of how severely armed conflicts have impacted natural resources, eco-systems, biodiversity and long-term implications on climate has massively improved over the last decade. Without a doubt, cataclysmic events such as the 1991 Gulf War oil fires contributed to raising awareness on the conflict-environment nexus, and the images of burning wells are engraved into our collective mind. But another more recent, under-examined yet major contributor to this growing cognizance is the digital revolution, which has provided us with a wealth of data and information from conflict-affected countries quickly made available through the internet. With just a few clicks, anyone with a computer or smartphone and a Wi-Fi connection can follow, often in near-real time, events shared through social media in warzones or satellite imagery showing what is unfolding on the ground.

These developments have significantly deepened our understanding of how military activities, both historically and in current conflicts, contribute to environmental damage and can impact the lives and livelihoods of civilians. Geospatial analysis through earth observation (EO) is now widely used to document international humanitarian law (IHL) violations, improve humanitarian response and inform post-conflict assessments.

These new insights on conflict-environment dynamics have driven humanitarian, military and political responses. The latter are essential for the protection of the environment in armed conflict: with knowledge and understanding also comes a responsibility to prevent, mitigate and minimize environmental damage, in line with existing international obligations. Of particular relevance, under international humanitarian law, militaries must take into account incidental environmental damage that is reasonably foreseeable based on an assessment of information from all sources available to them at the relevant time (ICRC Guidelines on the Protection of the Environment, Rule 7; Customary IHL Rule 43). Excessive harm is prohibited, and all feasible precautions must be taken to reduce incidental damage (Guidelines Rule 8, Customary IHL Rule 44).

How do we ensure that the data-driven strides forward in understanding conflict-driven environmental damage translate into proper military training and decision-making, humanitarian response and reconstruction efforts? How can this influence behaviour change and improve accountability for military actions and targeting decisions?…(More)”.

Next-generation nowcasting to improve decision making in a crisis

Curated on Posted on by Stefaan Verhulst

Frank Gerhard, Marie-Paule Laurent, Kyriakos Spyrounakos, and Eckart Windhagen at McKinsey: “In light of the limitations of the traditional models, we recommend a modified approach to nowcasting that uses country- and industry-specific expertise to boil down the number of variables to a selected few for each geography or sector, depending on the individual economic setting. Given the specific selection of each core variable, the relationships between the variables will be relatively stable over time, even during a major crisis. Admittedly, the more variables used, the easier it is to explain an economic shift; however, using more variables also means a greater chance of a break in some of the statistical relationships, particularly in response to an exogenous shock.

This revised nowcasting model will be more flexible and robust in periods of economic stress. It will provide economically intuitive outcomes, include the consideration of complementary, high-frequency data, and offer access to economic insights that are at once timely and unique.

Nowcast for Q1 2021 shows differing recovery speeds by sector and geography.

For example, consumer spending can be estimated in different US cities by combining data such as wages from business applications and footfall from mobility trend reports. As a more complex example: eurozone capitalization rates are, at the time of the writing of this article, available only through January 2021. However, a revamped nowcasting model can estimate current capitalization rates in various European countries by employing a handful of real-time and high-frequency variables for each, such as retail confidence indicators, stock-exchange indices, price expectations, construction estimates, base-metals prices and output, and even deposits into financial institutions. The choice of variable should, of course, be guided by industry and sector experts.

Similarly, published figures for gross value added (GVA) at the sector level in Europe are available only up to the second quarter of 2020. However, by utilizing selected variables, the new approach to nowcasting can provide an estimate of GVA through the first quarter of 2021. It can also highlight the different experiences of each region and industry sector in the recent recovery. Note that the sectors reliant on in-person interactions and of a nonessential nature have been slow to recover, as have the countries more reliant on international markets (exhibit)….(More)”.

Establishing a Data Trust: From Concept to Reality

Curated on Posted on by Stefaan Verhulst

Blog by Stefaan Verhulst, Aditi Ramesh & Andrew Young, Peter Rabley & Christopher Keefe: “As ever-more areas of our public and private lives succumb to a process of datafication, it is becoming increasingly urgent to find new ways of managing the data lifecycle: how data is collected, stored, used, and reused. In particular, legacy notions of control and data access need to be reimagined for the twenty-first century, in ways that give more prominence to the public good and common interests – in a manner that is responsible and sustainable. That is particularly true for mapping data which is why The GovLab and FutureState, with the support of The Rockefeller Foundation, are partnering with PLACE to assist them in designing a new operational and governance approach for creating, storing and accessing mapping data: a Data Trust. 

PLACE is a non-profit formed out of a belief that mapping data is an integral part of the modern digital ecosystem and critical to unlocking economic, social and environmental opportunities for sustainable and equitable growth, development and climate resiliency; however, this data is not available or affordable in too many places around the world. PLACE’s goal is to bridge this part of the digital divide.

Blog#1 Infographic B.png

Five key considerations inform the design of such a new framework:

  • Governing Data as a Commons: The work of Elinor Ostrom (among others) has highlighted models that go beyond private ownership and management. As a non-excludable and non-rivalrous asset, data fits this model well: one entity’s control or “ownership” of data doesn’t limit another entity’s (non-excludable); and one entity’s consumption or use of data doesn’t prevent another entity from similarly doing so (non-rivalrous). A new framework for governance would emphasize the central role of  “data as a commons.”
  • Avoiding a “Tragedy of the Commons”: Any commons is susceptible to a “tragedy of the commons”: a phenomenon in which entities or individuals free-ride on shared resources, depleting their value or usability for all, resulting in a failure to invest in maintenance, improvement and innovation and in the process contributing negatively to the public interest . Any reimagined model for data governance needs to acknowledge this risk, and build in methods and processes to avoid a tragedy of the commons and ensure “data sustainability.” As further described below we believe that sustainability can best be achieved through a membership model.
  • Tackling Data Asymmetries and Re-Distribution of Responsibilities: Everyone is a participant in today’s “data commons,” but not all stakeholders benefit equally. One way to ensure the sustainability of a data commons is to require that larger players—e.g., the most profitable platforms, and other entities that disproportionately benefit from network effects—assume greater responsibilities to maintain the commons. These responsibilities can take many forms—financial, technical know-how, regulatory or legal prowess—and will vary by entity and each entity’s specialization. The general idea is that all stakeholders should have equal rights and access—but some will have greater responsibilities and may be required to contribute more.
  • Independent Trustees and Strong Engagement: Who should govern the data as a commons? Another way to avoid a tragedy of the commons is to ensure that a clear set of rules, principles and guidelines determine what is acceptable (and not), and what constitutes fair play and reasonable data access and use. These guidelines should be designed and administered by independent trustees, whose responsibilities, powers, terms and selection mechanisms are clearly defined and bounded. The trustees should be drawn from across geographies and sectors, representing as wide a range of interests and expertise as possible.In addition, trustees should steer responsible data access in a manner that is informed by input from experts, stakeholders, data subjects, and intended beneficiaries, using innovative ways of engagement and deliberations.
  • Inclusion and Protection: A data trust designed for the commons must “work” for all and especially the most vulnerable and marginalized among us. The identity of some people and communities is inextricably linked to location and, therefore, requires us to be especially mindful of the risks of abuse for such communities. How can we prevent surveillance or bias against indigenous groups, for example? Equally important, how can we empower communities with more understanding of and voice in how data is collected and used about their place? Such communities are front-and-center in the design of the Trust and its governance….(More)”.

Three ways to supercharge your city’s open-data portal

Curated on Posted on by Stefaan Verhulst

Bloomberg Cities: “…Three open data approaches cities are finding success with:

Map it

Much of the data that people seem to be most interested in is location-based, local data leaders say. That includes everything from neighborhood crime stats and police data used by journalists and activists to property data regularly mined by real estate companies. Rather than simply making spatial data available, many cities have begun mapping it themselves, allowing users to browse information that’s useful to them.

At atlas.phila.gov, for example, Philadelphians can type in their own addresses to find property deeds, historic photos, nearby 311 complaints and service requests, and their polling place and date of the next local election, among other information. Los Angeles city’s GeoHub collects maps showing the locations of marijuana dispensaries, reports of hate crimes, and five years of severe and fatal crashes between drivers and bikers or pedestrians, and dozens more.

A CincyInsights map highlighting cleaned up greens-aces across the city.
A CincyInsights map highlighting cleaned up green spaces across the city.

….

Train residents on how to use it

Cities with open-data policies learn from best practices in other city halls. In the last few years, many have begun offering trainings to equip residents with rudimentary data analysis skills. Baton Rouge, for example, offered a free, three-part Citizen Data Academy instructing residents on “how to find [open data], what it includes, and how to use it to understand trends and improve quality of life in our community.” …

In some communities, open-data officials work with city workers and neighborhood leaders to learn to help their communities access the benefits of public data even if only a small fraction of residents are accessing the data itself.

In Philadelphia, city teams work with the Citizens Planning Institute, an educational initiative of the city planning commission, to train neighborhood organizers in how to use city data around things like zoning and construction permits to keep up with development in their neighborhoods, says Kistine Carolan, open data program manager in the Office of Innovation and Technology. The Los Angeles Department of Neighborhood Empowerment runs a Data Literacy Program to help neighborhood groups make better use of the city’s data. So far, officials say, representatives of 50 of the city’s 99 neighborhood councils have signed up as part of the Data Liaisons program to learn new GIS and data-analysis skills to benefit their neighborhoods. 

Leverage the COVID moment

The COVID-19 pandemic has disrupted cities’ open-data plans, just like it has complicated every other aspect of society. Cities had to cancel scheduled in-person trainings and programs that help them reach some of their less-connected residents. But the pandemic has also demonstrated the fundamental role that data can play in helping to manage public emergencies. Cities large and small have hosted online tools that allow residents to track where cases are spiking—tools that have gotten many new people to interact with public data, officials say….(More)”.

Side-Stepping Safeguards, Data Journalists Are Doing Science Now

Curated on Posted on by Stefaan Verhulst

Article by Irineo Cabreros: “News stories are increasingly told through data. Witness the Covid-19 time series that decorate the homepages of every major news outlet; the red and blue heat maps of polling predictions that dominate the runup to elections; the splashy, interactive plots that dance across the screen.

As a statistician who handles data for a living, I welcome this change. News now speaks my favorite language, and the general public is developing a healthy appetite for data, too.

But many major news outlets are no longer just visualizing data, they are analyzing it in ever more sophisticated ways. For example, at the height of the second wave of Covid-19 cases in the United States, The New York Times ran a piece declaring that surging case numbers were not attributable to increased testing rates, despite President Trump’s claims to the contrary. The thrust of The Times’ argument was summarized by a series of plots that showed the actual rise in Covid-19 cases far outpacing what would be expected from increased testing alone. These weren’t simple visualizations; they involved assumptions and mathematical computations, and they provided the cornerstone for the article’s conclusion. The plots themselves weren’t sourced from an academic study (although the author on the byline of the piece is a computer science Ph.D. student); they were produced through “an analysis by The New York Times.”

The Times article was by no means an anomaly. News outlets have asserted, on the basis of in-house data analyses, that Covid-19 has killed nearly half a million more people than official records report; that Black and minority populations are overrepresented in the Covid-19 death toll; and that social distancing will usually outperform attempted quarantine. That last item, produced by The Washington Post and buoyed by in-house computer simulations, was the most read article in the history of the publication’s website, according to Washington Post media reporter Paul Farhi.

In my mind, a fine line has been crossed. Gone are the days when science journalism was like sports journalism, where the action was watched from the press box and simply conveyed. News outlets have stepped onto the field. They are doing the science themselves….(More)”.